Public record
Software health reportschema 0.16.0 · metrics 2.10.0 · 2026-07-21 01:32 UTC

econchick / interrogate

Explain yourself! Interrogate a codebase for docstring coverage.

PythonMIT★ 671 stars⑂ 53 forkssince Apr 2020View on GitHub ↗

econchick/interrogate holds a health index of 57 out of 100, placing it in the Moderate band. It scores highest on Engineering Quality (80/100) and lowest on Vitality (28/100). It was last updated 49 days ago. A single contributor accounts for most of its recent work.

57
overall / 100
Moderate

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

57
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 55 is calibrated to 57 on the published index scale (record calibration 2026-08-02).

Ownership

Lynn RootPersonal account
1,236 followers76 public repossince Nov 2011@spotify

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

28At Risk · 21% of overall
How it's scored
18/36Push recencylast push 49 days ago
0/36Commit cadence0/52 weeks with commits
0/18Commit volume0 commits in the last year
0/10OpenSSF Scorecard: Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
Inputs used
commits_last_year0
human_commit_share
days_since_last_push49
active_weeks_last_year0
How it's scored
27/27Ships releases1 releases published
0/36Release recencylatest release 834 days ago
12.6/27Release cadencecadence unknown (single release)
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count1
latest_release_tag1.7.0
releases_from_tagsno
days_since_latest_release834
mean_days_between_releases
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

77Good · 17% of overall
How it's scored
45.8/60Stars671 stars
14.3/25Forks53 forks
3.3/15Watchers5 watchers
Inputs used
forks53
stars671
watchers5
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_services
has_pull_request_templateyes

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

51Moderate · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
3.7/22.5Commit distributiontop contributor authored 84% of commits
13.5/13.5Contributor breadth17 contributors
10/10OpenSSF Scorecard: Contributorsproject has 10 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled17
top_contributor_share0.835
How it's scored
24.7/42Issue resolution59% of issues closed
18.2/30PR acceptance69/114 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-ReviewFound 1/12 approved changesets -- score normalized to 0
Inputs used
merged_prs69
open_issues26
closed_issues37
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.587
closed_unmerged_prs45
first_time_authors_30d
first_time_prs_merged_30d
first_time_prs_decided_30d
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
10/30Ownership backingpersonal (user) account
0/20Verified domainnot applicable to user accounts
22.2/25Owner reach1,236 followers of econchick
25/25Track record76 public repos, account ~14 yr old
Inputs used
followers1,236
owner_typeUser
is_verified
owner_logineconchick
public_repos76
account_age_days5,364
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Engineering Quality

Are baseline engineering and documentation practices in place?

80Excellent · 19% of overall
How it's scored
24/24CI workflows4 workflow(s)
24/24Tests present
16/16Linter configtox.ini
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests0 out of 7 merged PRs checked by a CI test -- score normalized to 0
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://interrogate.readthedocs.io
10/10Repository description
10/10Topics5 topics
0/10Wiki
Inputs used
topicspython, documentation, coverage, code-quality, hacktoberfest
has_wikino
homepagehttps://interrogate.readthedocs.io
docs_sitehttps://interrogate.readthedocs.io
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

44Weak · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
0/2.5CI-Tests0 out of 7 merged PRs checked by a CI test -- score normalized to 0
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0/7.5Code-ReviewFound 1/12 approved changesets -- score normalized to 0
2.5/2.5Contributorsproject has 10 contributing companies or organizations
10/10Dangerous-Workflowno dangerous workflow patterns detected
7.5/7.5Dependency-Update-Toolupdate tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
0/7.5Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
0/5Packagingno data
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0/5SASTSAST tool is not run on all commits -- score normalized to 0
0/5Security-Policysecurity policy file not detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate4.4
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging, Signed-Releases. Remaining weights renormalized.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

48Weak · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
0/40Legible commit historyno data
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share
agent_instruction_files
agent_instruction_max_bytes
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.
How it's scored
18/18One-command bootstrapdocs/Makefile
22/22Automated tests
11/11Lint / format configtox.ini
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practiceno data
5/8Automated maintenancedependency automation configured, none observed in the sampled commits
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share
toolchain_manifests
dependency_bot_commit_share0
Excluded from scoring (no data or not applicable): Demonstrated agent practice. Remaining weights renormalized.
How it's scored
0/45Type-checkable codePython without a type-check config
55/55Manageable file sizes0/23 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes20,374
source_files_sampled23
oversized_source_files0
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplessample
Inputs used
example_dirssample
has_mcp_signalno
api_schema_files
interfaces_expected_of
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

671GitHub stars
17contributors
0commits, last 12 months
49days since last push
1releases
1bus factor
26open issues
PyPIpackage ecosystems

Data collection warnings

  • No resolved dependencies carried a version and a supported ecosystem

More detail

OpenSSF Scorecard 4.4 / 10
4.4aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-07-21 01:32 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
0CI-Tests0 out of 7 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 1/12 approved changesets -- score normalized to 0
10Contributorsproject has 10 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
All dependencies 6

Full resolved dependency set from the GitHub dependency graph: 0 direct and 6 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIattrsindirect
PyPIclickindirect
PyPIcoloramaindirect
PyPIpyindirect
PyPItabulateindirect
PyPItomliindirect
Dependency advisories not assessed

Advisory matching could not run for this report: No resolved dependencies carried a version and a supported ecosystem

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.16.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics.